Bridging the Semantic Gap Between Diagnostic Histopathology and Image Analysis.

With the wider acceptance of Whole Slide Images (WSI) in histopathology domain, automatic image analysis algorithms represent a very promising solution to support pathologist's laborious tasks during the diagnosis process, to create a quantification-based second opinion and to enhance inter-observer agreement. In this context, reference vocabularies and formalization of the associated knowledge are especially needed to annotate histopathology images with labels complying with semantic standards. In this work, we elaborate a sustainable triptych able to bridge the gap between pathologists and image analysis scientists.

A workflow to process 3D+time microscopy images of developing organisms and reconstruct their cell lineage.

The quantitative and systematic analysis of embryonic cell dynamics from in vivo 3D+time image data sets is a major challenge at the forefront of developmental biology. Despite recent breakthroughs in the microscopy imaging of living systems, producing an accurate cell lineage tree for any developing organism remains a difficult task. We present here the BioEmergences workflow integrating all reconstruction steps from image acquisition and processing to the interactive visualization of reconstructed data.

A digital framework to build, visualize and analyze a gene expression atlas with cellular resolution in zebrafish early embryogenesis.

A gene expression atlas is an essential resource to quantify and understand the multiscale processes of embryogenesis in time and space. The automated reconstruction of a prototypic 4D atlas for vertebrate early embryos, using multicolor fluorescence in situ hybridization with nuclear counterstain, requires dedicated computational strategies. To this goal, we designed an original methodological framework implemented in a software tool called Match-IT.

Reversible adaptive trees.

We describe reversible adaptive trees, a class of stochastic algorithms modified from the formerly described adaptive trees. They evolve in time a finite subset of an ambient Euclidean space of any dimension, starting from a seed point and, accreting points to the evolving set, they grow branches towards a target set which can depend on time. In contrast with plain adaptive trees, which were formerly proven to have strong convergence properties to a static target, the points of reversible adaptive trees are removed from the tree when they have not been used recently enough in a path from the root to an accreted point.

Planning and tracking chemotherapy production for cancer treatment: a performing and integrated solution.

Purpose: Chemotherapy drugs are intended for the treatment of cancer. The production of such drugs and their administration to the patient is a delicate and expensive operation. The study deals with the acquisition and processing of data regarding the production of intravenous chemotherapy, from the production request (the medical prescription), the production itself (pharmaceutical process), to the delivery in the health care unit, for the administration of the chemotherapy.

Towards iconic language for patient records, drug monographs, guidelines and medical search engines.

Practicing physicians have limited time for consulting medical knowledge and records. We have previously shown that using icons instead of text to present drug monographs may allow contraindications and adverse effects to be identified more rapidly and more accurately. These findings were based on the use of an iconic language designed for drug knowledge, providing icons for many medical concepts, including diseases, antecedents, drug classes and tests.

Distance-driven adaptive trees in biological metric spaces: uninformed accretion does not prevent convergence.

We present several variants of a stochastic algorithm which all evolve tree-structured sets adapted to the geometry of general target subsets in metric spaces, and we briefly discuss their relevance to biological modelling. In all variants, one repeatedly draws random points from the target (step 1), each time selecting from the tree to be grown the point which is closest to the point just randomly drawn (step 2), then adding to the tree a new point in the vicinity of that closest point (step 3 or accretion step). The algorithms differ in their accretion rule, which can use the position of the target point drawn, or not.

Adaptive branching in evolution and epigenesis.

We describe one of the simplest models that exhibit an adaptive branching behaviour. It is analysed both experimentally and formally, and its successive bifurcations provide a good model of what R. Thom called 'generalized catastrophes'.

Matching a surface to a given sectional curve.

We describe an algorithm to position a rigid surface so as to make its cross-section by a given plane match a given curve in that plane, a problem relevant to model-based medical imaging. After building an atlas of cross-sections of the surface and searching it for a best position to start from, each iteration of the algorithm (1) determines a vector field along the intersection curve that will improve its matching with the target curve, and (2) computes and applies a small displacement of the surface whose effect on the intersection will approximate best the required vector field. Computations use least-square techniques, an exponential formula for Lie groups of transformations, and generic properties of cross-sections.

Memorization and delayed expression of regulatory messages in plants.

Plantlets of Bidens pilosus L., considered to be basically symmetrical, can be "lateralized" (A/B) by being administered a "symmetry-breaking" signal such as puncturing one of the plant cotyledons. The induced asymmetry remains latent as long as the plants have not been made "permissive", i.